Nonstationary Precipitation

Frequency Estimates for Texas

JAMES DOSS-GOLLIN

Rice Civil & Environmental Engineering

Yuchen Lu (Rice)

Benjamin Seiyon Lee (GMU)

John Nielsen-Gammon (TAMU)

Rewati Niraula (TWDB)

@AGU 2023, NH14B-07

…for better or for worse,

IDF CURVES UNDERPIN RISK ASSESSMENT

James Doss-Gollin

Bates et al. (2021) fig. 8

Mark Wolfe/FEMA News

EXISTING GUIDANCE LEAVES GAPS

THE CLIMATE IS CHANGING BUT SAMPLING VARIABILITY CHALLENGES TREND ESTIMATION

Fagnant et al. (2020): each line is a gauge from the same \(5^\circ \times 3^\circ\) region

LET’S JUST USE ESMS?

Better sample weather given climate

Physical constraints improve projection

Drizzle bias and dynamical limitations motivate downscaling / bias correction still need a statistical model!

NONSTATIONARY MODELS

NEED MORE PARAMETERS

Generic nonstationary model for annual maximum precipitation: \[ y(\mathbf{s}, t) \sim \text{GEV} \left( \mu(\mathbf{s}, t), \sigma(\mathbf{s}, t), \xi(\mathbf{s}, t) \right) \]

Process-informed models condition parameters on climate indices \(\mathbf{x}(t)\) (Cheng & AghaKouchak, 2014; Schlef et al., 2023) \[ \theta(\mathbf{s}, t) = \alpha + \underbrace{\sum_{j=1}^J \beta_j(\mathbf{s}) x_j(t)}_\text{additional parameters} \]

NONSTATIONARY MODELS

INCREASE ESTIMATION UNCERTAINTY

More parameters, same data more uncertainty (Serinaldi & Kilsby, 2015)

BETTER DATA 🤝 BETTER STATS

insert here: map of gauges used

Long-record daily gauges, new mesonets, and more

  • framework: Bayesian hierarchical model (flexible, probabilistic)
  • hypothesis: parameters are smooth
  • model: latent parameters as spatial fields (Moran basis functions)

POSITIVE TRENDS FOR MOST OF TEXAS

WE FIND HIGHER HAZARD THAN ATLAS-14 EXCEPT IN HARVEY-IMPACTED AREAS

UNCERTAINTY ; CALIBRATION

Spatially Varying Covariates Model

Estimates for ungauged locations & future years

Pool information to reduce uncertainty & improve calibration

Resolve sampling variability or deep uncertainties

For more, see Yuchen Lu’s poster H21T-1602 on Tuesday morning

References

Bates, P. D., Quinn, N., Sampson, C., Smith, A., Wing, O., Sosa, J., et al. (2021). Combined modeling of US fluvial, pluvial, and coastal flood hazard under current and future climates. Water Resources Research, 57(2), e2020WR028673. https://doi.org/10.1029/2020WR028673
Cheng, L., & AghaKouchak, A. (2014). Nonstationary precipitation intensity-duration-frequency curves for infrastructure design in a changing climate. Scientific Reports, 4(1), 7093. https://doi.org/10.1038/srep07093
Fagnant, C., Gori, A., Sebastian, A., Bedient, P. B., & Ensor, K. B. (2020). Characterizing spatiotemporal trends in extreme precipitation in Southeast Texas. Natural Hazards, 104(2), 1597–1621. https://doi.org/10.1007/s11069-020-04235-x
Schlef, K. E., Kunkel, K. E., Brown, C., Demissie, Y., Lettenmaier, D. P., Wagner, A., et al. (2023). Incorporating non-stationarity from climate change into rainfall frequency and intensity-duration-frequency (IDF) curves. Journal of Hydrology, 616, 128757. https://doi.org/10.1016/j.jhydrol.2022.128757
Serinaldi, F., & Kilsby, C. G. (2015). Stationarity is undead: Uncertainty dominates the distribution of extremes. Advances in Water Resources, 77, 17–36. https://doi.org/10.1016/j.advwatres.2014.12.013

@jdossgollin